# app.py (النسخة النهائية - مع تصحيح SyntaxError الثاني)
import gradio as gr
import numpy as np
import random
import torch
from diffusers import DiffusionPipeline
import time
# --- 1. Settings and Constants ---
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
DTYPE = torch.float16 if torch.cuda.is_available() else torch.float32
MODEL_ID = "YourUsername/Takween-v1"
BASE_MODEL_ID = "runwayml/stable-diffusion-v1-5"
MAX_SEED = np.iinfo(np.int32).max
LOGO_SVG = """
"""
# --- 2. Model Loading ---
try:
pipe = DiffusionPipeline.from_pretrained(MODEL_ID, torch_dtype=DTYPE, safety_checker=None)
print(f"✅ Trained model '{MODEL_ID}' loaded successfully.")
except Exception:
print(f"❌ Could not load trained model '{MODEL_ID}'. Loading base model.")
pipe = DiffusionPipeline.from_pretrained(BASE_MODEL_ID, torch_dtype=DTYPE, safety_checker=None)
pipe = pipe.to(DEVICE)
# --- 3. Professional Theme (Golden Version) ---
theme = gr.themes.Base(
primary_hue=gr.themes.colors.amber,
secondary_hue=gr.themes.colors.neutral,
font=[gr.themes.GoogleFont("IBM Plex Sans"), "system-ui", "sans-serif"],
).set(
body_background_fill="*neutral_50",
block_background_fill="white",
block_border_width="1px",
block_shadow="*shadow_drop_lg",
button_primary_background_fill="*primary_500",
button_primary_background_fill_hover="*primary_600",
)
# --- 4. Inference Function with UI Updates (Corrected) ---
def infer(prompt, negative_prompt, guidance_scale, num_inference_steps, seed, randomize_seed):
if randomize_seed:
seed = random.randint(0, MAX_SEED)
# =======================================================
# <<< تم تعديل هذا الجزء لحل مشكلة SyntaxError >>>
# الخطوة 1: إنشاء المولد على الجهاز الصحيح
generator = torch.Generator(device=DEVICE)
# الخطوة 2: تحديد البذرة للمولد
generator.manual_seed(seed)
# =======================================================
yield {
output_image: gr.update(value=None, interactive=False, visible=True),
run_button: gr.update(interactive=False, value="Generating..."),
}
image = pipe(
prompt=prompt,
negative_prompt=negative_prompt,
guidance_scale=guidance_scale,
num_inference_steps=int(num_inference_steps),
generator=generator,
).images[0]
yield {
output_image: gr.update(value=image, interactive=True),
output_seed: gr.update(value=seed),
run_button: gr.update(interactive=True, value="Generate Again"),
}
# --- 5. Professional UI Layout ---
with gr.Blocks(theme=theme, css="#footer {text-align: center;}") as demo:
with gr.Row():
gr.HTML(f"
{LOGO_SVG}
Takween Project
")
gr.Markdown("#### A specialized model for generating precise geometric images from text descriptions.")
gr.HTML("")
with gr.Row():
with gr.Column(scale=1):
prompt = gr.Textbox(label="Prompt", placeholder="A red circle with thick black borders...", lines=3)
negative_prompt = gr.Textbox(label="Negative Prompt", placeholder="Low quality, blurry, distorted...")
with gr.Accordion("Advanced Settings", open=False):
guidance_scale = gr.Slider(label="Guidance Scale", minimum=1.0, maximum=20.0, value=7.5, step=0.1)
num_inference_steps = gr.Slider(label="Number of Steps", minimum=10, maximum=100, value=30, step=1)
with gr.Row():
seed = gr.Number(label="Seed", value=0, precision=0)
randomize_seed = gr.Checkbox(label="Randomize", value=True)
run_button = gr.Button("Generate Image", variant="primary")
gr.Examples(examples=["A filled red circle with a thick black border", "An outline blue triangle positioned to the left of a yellow square", "A green star overlapping a purple rectangle"], inputs=[prompt])
with gr.Column(scale=2):
output_image = gr.Image(label="Generated Image", interactive=False, height=512)
output_seed = gr.Textbox(label="Seed Used", interactive=False)
gr.HTML("")
with gr.Accordion("Team and Acknowledgments", open=False):
gr.Markdown("""
Development Team:
Osama Saeed
Tareq Al-Omari
Special Thanks:
We extend our sincere gratitude for the guidance and support of:
Dr. Akram Al-Sabari (Professor of AI and Machine Learning)